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join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) develops explainable, autonomous, and multimodal artificial intelligence (AI) systems to advance biological discovery. Our research integrates
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more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Development of statistical methods for estimating plant population size and change Mathematical
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learn about carcinogenic mutagens (https://www.biorxiv.org/content/10.1101/2023.12.06.570467v1 ), while studying the spatial genetic heterogeneity of tumors tells us about the tumor mode of growth (https
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of GIS, spatial statistics, or other spatially relevant methods. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with
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Foundation (FAPESP), and combines statistical analysis, spatial methods, and qualitative research. Georeferenced data from the Military Police and the Municipal Secretariat of Urban Security will be used
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maintain algorithms in biomedical image analysis pipelines. Validate algorithm performance on novel datasets. Analyze spatial transcriptomic data. Perform statistical analyses on data extracted form bio
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, the individual will: 1) Analyze acoustic data with existing software (Echoview, R, Python). 2) Collaborate with USGS scientists on field data collections. 3) Investigate the spatial patterns of fish and
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in any of the following areas would be highly valued: prior work on fishery community profiling using established methodology; scholarly review and compiling datasets; statistical and spatial analysis
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various data types such as whole-genome sequencing (WGS), whole-exome sequencing (WES), RNA sequencing, spatial data, etc, and will also include rich clinical annotation to explore the clinical implications
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statistical and epidemiological methods Working experience with data analysis software, such as Stata, SPSS, R, or Mplus Experience in spatial and temporal data analysis using GIS, including RStudio/Python